Better machine learning results do not always come from changing the model.
A lot of the time, they come from building better features.
I just published a post on AI feature engineering techniques, including automated feature synthesis, embeddings, dimensionality reduction, and feature selection, and why they still matter in modern ML workflows.
https://aitransformer.online/ai-feature-engineering-techniques/
#MachineLearning #DataScience #AI #FeatureEngineering #MLOps
How I Built a Machine Learning Tool to Predict Drug Manufacturing Failures
A bioprocess engineer's journey into machine learning and why the pharmaceutical industry desperately needs this bridge When I tell people I work in bioprocess engineering, I usually get blank stares. When I explain that I help manufacture proteins in giant tanks for therapeutic use, the response is often: "Oh, like brewing beer?" Not quite. But close enough. What I don't usually mention is that I've been teaching myself machine learning on nights and weekends. Not because it's trendy, but […]Feature Engineering: Event Data → Snapshot Features with merge_asof()
If you join events to the “latest” snapshot the wrong way, you leak future data.
This post shows how to build point-in-time features using merge_asof() (proper keys, sorting, tolerance, and clean tests) with Python examples.
#Python #Pandas #FeatureEngineering #DataScience #MachineLearning
@chartrdaily @pythonclcoding @theartificialintelligence @programming @towardsdatascience @python
The Biggest Feature Engineering Mistakes in Fraud Models
(Events + Snapshot tables, “as-of” features, clean splits, and Python outputs)
Most “great” AUC scores die in production because the features leaked time.
This post covers: event vs snapshot data, proper as-of joins, and clean evaluation splits—with Python outputs.
#MachineLearning #FraudDetection #DataScience #FeatureEngineering #Python
Before diving into deep learning hype, remember the power of classic algorithms. Linear regression, decision trees, and thoughtful feature engineering still drive real‑world analytics and revenue. Master these fundamentals and your neural nets will perform better, faster, and cheaper. Curious how the basics outpace the buzz? Read on. #NeuralNetworks #LinearRegression #DecisionTrees #FeatureEngineering
🔗 https://aidailypost.com/news/master-fundamentals-before-neural-networks-core-algorithms-power
"It is not uncommon for an analyst to conduct a supervised analysis of data to detect which predictors are significantly associated with the outcome. These significant predictors are then used in a visualization (such as a heat map or cluster analysis) on the same data. Not surprisingly, the visualization reliably demonstrates clear patterns between the outcomes and predictors and appears to provide evidence of their importance. However, since the same data are shown, the visualization is essentially cherry picking the results that are only true for these data and which are unlikely to generalize to new data."
Wrote Max Kuhn @topepo and Kjell Johnson, 2019, in "Feature Engineering and Selection: A Practical Approach for Predictive Models" https://bookdown.org/max/FES/
#correlations #NoFreeLunch #electricity #agriculture #livestock #renewables #dataViz #emissions #GHG #methane #GreenhouseForcing #dataScience #featureEngineering #correlation
Want to catch hidden seasonality and drift in your data? Plotting timestamps reveals trends, class‑imbalance shifts, and lets you build a robust feature set for production models. Our latest research shows how temporal patterns boost performance—open‑source tools included. Dive in to see the visual tricks that keep your ML pipelines ahead. #TemporalPatterns #Seasonality #FeatureEngineering #ModelProduction
🔗 https://aidailypost.com/news/use-temporal-patterns-plot-timestamps-spot-seasonality-trends-shifts
#AIEngineering #featureengineering #cheatsheet
https://machinelearningmastery.com/10-python-one-liners-for-calculating-model-feature-importance/
10 simple but effective Python one-liners to calculate model feature importance from different perspectives, enabling not only understanding of how your machine learning model behaves, but also why it predicts the way it does.
Scott McMahan
Kemal
Hasan Ali Gültekin
AI Daily Post
Eric Maugendre about data
bayo
aicoder
HackerNoon